{"id":"W2324352070","doi":"10.1139/gen-2016-0039","title":"Multi-taxa integrated landscape genetics for zoonotic infectious diseases: deciphering variables influencing disease emergence","year":2016,"lang":"en","type":"article","venue":"Genome","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biological dispersal; Biology; Interspecific competition; Disease; Identification (biology); Population; Ecology; Evolutionary biology; Landscape epidemiology; Landscape ecology; Habitat","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009540057,0.0004339054,0.0005921592,0.0009327666,0.0003819373,0.00115026,0.0006376134,0.0006264583,0.00082325],"category_scores_gemma":[0.002636472,0.0002249627,0.0007930184,0.000647133,0.0006857119,0.001188639,0.0006830495,0.0007811327,0.00004752218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009858932,"about_ca_system_score_gemma":0.0006116322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006606049,"about_ca_topic_score_gemma":0.01073142,"domain_scores_codex":[0.9996982,0.0001667717,0.00001000268,0.00007420847,0.00002174766,0.00002920022],"domain_scores_gemma":[0.9990453,0.0005847504,0.0002033757,0.00005176912,0.00003493919,0.00007979543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001349522,0.0002026926,0.1580688,0.0001898954,0.0005081922,0.0003443799,0.00035039,0.7299258,0.02456352,0.04058613,0.0003843136,0.04474097],"study_design_scores_gemma":[0.0000184173,0.00006639939,0.02996059,0.00001486293,0.00006942197,0.00009201102,0.0001078844,0.9504774,0.0004528682,0.01837592,0.0003448678,0.00001939509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7746504,0.0005516814,0.2226793,0.0004691589,0.00001861265,0.00004726246,0.0001895049,0.00009173775,0.001302249],"genre_scores_gemma":[0.9711913,0.0001674326,0.0282559,0.00005503545,0.000007493923,0.00003030177,0.00007941832,0.00001590241,0.0001970049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006606049,"threshold_uncertainty_score":0.01313519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940155971985746,"score_gpt":0.27858598853086,"score_spread":0.2591844288110026,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}